trying to get sac running

lerobot/configs/policy/sac_pusht_keypoints.yaml

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+# @package _global_
+
+# Train with:
+#
+# python lerobot/scripts/train.py \
+#   env=pusht \
+#   +dataset=lerobot/pusht_keypoints
+
+seed: 1
+dataset_repo_id: lerobot/pusht_keypoints
+
+training:
+  offline_steps: 0
+
+  # Offline training dataloader
+  num_workers: 4
+
+  batch_size: 128
+  grad_clip_norm: 10.0
+  lr: 3e-4
+
+  eval_freq: 10000
+  log_freq: 500
+  save_freq: 50000
+
+  online_steps: 1000000
+  online_rollout_n_episodes: 10
+  online_rollout_batch_size: 10
+  online_steps_between_rollouts: 1000
+  online_sampling_ratio: 1.0
+  online_env_seed: 10000
+  online_buffer_capacity: 40000
+  online_buffer_seed_size: 0
+  do_online_rollout_async: false
+
+  delta_timestamps:
+    observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
+    observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
+    action: "[i / ${fps} for i in range(${policy.horizon})]"
+    next.reward: "[i / ${fps} for i in range(${policy.horizon})]"
+
+policy:
+  name: sac
+
+  pretrained_model_path:
+
+  # Input / output structure.
+  n_action_repeats: 1
+  horizon: 5
+  n_action_steps: 5
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.environment_state: [16]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.environment_state: min_max
+    observation.state: min_max
+  output_normalization_modes:
+    action: min_max
+
+  # Architecture / modeling.
+  # Neural networks.
+  # image_encoder_hidden_dim: 32
+  discount: 0.99
+  temperature_init: 1.0
+  num_critics: 2
+  num_subsample_critics: None
+  critic_lr: 3e-4
+  actor_lr: 3e-4
+  temperature_lr: 3e-4
+  critic_target_update_weight: 0.005
+  utd_ratio: 2
+
+
+  # # Loss coefficients.
+  # reward_coeff: 0.5
+  # expectile_weight: 0.9
+  # value_coeff: 0.1
+  # consistency_coeff: 20.0
+  # advantage_scaling: 3.0
+  # pi_coeff: 0.5
+  # temporal_decay_coeff: 0.5
+  # # Target model.
+  # target_model_momentum: 0.995